Hello, I am using latent growth curve modeling to look at changes in alcohol use among college students with three waves of data. I am getting a negative error variance when i run an unconditional model (i.e., intercept and slope only). The univariates on my drinking variable show a potential floor effect at all 3 timepoints, yet only the error variance at wave 3 is negative. Any suggestions on how to interpret this and how to correct it?

Negative residual variances often occur when you have strong floor effects. There are two possible solutions: (1) hold the residual variances equal across time and (2) use two-part growth modeling as shown in Example 6.16 in the Mplus User's Guide.

My analysis also show negative residual for one particular variable (level 1) at a particular time point at level 2 (classroom). However, this residual is very small (-.003). I then set it to zero, is that ok? I am also wondering the output of intercept is all zero for LGM. If free one of the intercept, what does that mean? Does it mean that at at particular time point, the y is so different from other time points that it cannot be fully measured by the latent factor?